Paper Title
FRAUD DETECTION IN MOBILE RANKING WITH MACHINE LEARNING TECHNIQUESAbstract
Ranking fraud in the mobile App market refers to fraudulent or deceptive activities which have a purpose of bumping up the Apps in the popularity list. Indeed, it becomes more and more frequent for App develops to use shady means, such as inflating their Apps\' sales or posting phony App ratings, to commit ranking fraud. While the importance of preventing ranking fraud has been widely recognized, there is limited understanding and research in this area. To this end, in this paper, we provide a holistic view of ranking fraud and propose a ranking fraud detection system for mobile Apps. Specifically, we investigate two types of evidences, ranking based evidences and rating based evidences, by modeling Apps\' ranking and rating behaviors through statistical hypotheses tests. In addition, we propose an optimization based aggregation method to integrate all the evidences for fraud detection. The mobile industry is growing rapidly, subsequently the number of mobile apps coming in the market is also increasing. Google Play and Apple are app stores where apps can be downloaded by either paying or they can be downloaded free of cost. Rank of an app is important, as high ranked apps get noticed by customers more easily than those ranked low. In order to get a high rank some developers are resorting to fraudulent means in order to increase their app’s rank. Hence a ranking fraud detection system is required to detect rankings obtained through fraudulent means. We design and develop a ranking fraud detection system to detect fraud ranks.
KEYWORDS : Ranking fraud detection, Reviews, Ranking, Data mining, Mobile applications.